Related work

The foundational work on continual learning, 1988 to 2026: methods, theory, benchmarks, surveys, and the neuroscience of memory. Updated weekly; some carry our notes.

5 papers of 6,984Sort Recent · Most cited
  1. 2025
    Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised LearningWenzhuo Liu, Fei Zhu, Cheng‐Lin LiuTNNLS · Beijing Academy of Artificial Intelligence · University of Chinese Academy of Sciences · +1
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  2. 2025
    Dual Balanced Class-Incremental Learning With im-Softmax and Angular RectificationRuicong Zhi, Yicheng Meng, Junyi Hou, Jun WanTNNLS · University of Science and Technology Beijing · National University of Singapore · +4
  3. 2025
    Imbalance Mitigation for Continual Learning via Knowledge Decoupling and Dual Enhanced Contrastive LearningZhong Ji, Zhanyu Jiao, Qiang Wang … Jungong HanTNNLS · Tianjin University · Beijing Academy of Artificial Intelligence · +2
  4. 2024
    Online Active Continual Learning for Robotic Lifelong Object RecognitionXiangli Nie, Zhiguang Deng, Mingdong He … Zheng TangTNNLS · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +5
  5. 2021
    Incremental Concept Learning via Online Generative Memory RecallHuaiyu Li, Weiming Dong, Bao-Gang HuTNNLS · Chinese Academy of Sciences · Beijing Academy of Artificial Intelligence · +2
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About this index

We keep this list because we read the field and wanted one place to see it. It covers work on continual learning itself, in the core areas of machine learning, and leaves out papers that apply it inside another field, such as medical imaging or fault diagnosis. It lists only papers we have a reason to trust: published at a venue like NeurIPS, ICML, ICLR, CVPR or TPAMI, or led by someone who has published there, or cited a few hundred times. Preprints that later get accepted, and authors who later publish at those venues, are picked up by the weekly run. It is seeded from the community lists kept by ContinualAI and by Xialei Liu, then filled out from OpenAlex, and every week a script looks for new papers on OpenAlex and arXiv. A model reads each candidate and decides whether it belongs; a person reviews the additions before they go live. Authors and affiliations come from OpenAlex, so a recent preprint can lack its institutions for a week or two.

Missing something, or filed under the wrong venue? Write to hello@unify.ai with the arXiv id or DOI.